| // |
| // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonLstmFloatWorkload.hpp" |
| #include "NeonWorkloadUtils.hpp" |
| |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| #include <aclCommon/ArmComputeUtils.hpp> |
| |
| #include <armnn/utility/NumericCast.hpp> |
| |
| #include "neon/NeonTensorHandle.hpp" |
| |
| namespace armnn |
| { |
| using namespace armcomputetensorutils; |
| |
| NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor& descriptor, const WorkloadInfo& info) |
| : FloatWorkload<LstmQueueDescriptor>(descriptor, info) |
| { |
| // Report Profiling Details |
| ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonLstmFloatWorkload_Construct", |
| descriptor.m_Parameters, |
| info, |
| GetGuid()); |
| |
| arm_compute::LSTMParams<arm_compute::ITensor> lstm_param; |
| |
| // Basic parameters |
| m_InputToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo()); |
| |
| m_InputToCellWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo()); |
| |
| m_InputToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo()); |
| |
| m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo()); |
| |
| m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo()); |
| |
| m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo()); |
| |
| m_ForgetGateBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo()); |
| |
| m_CellBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo()); |
| |
| m_OutputGateBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo()); |
| |
| // for future reference: check the AndroidNN API for the logic here |
| if (!m_Data.m_Parameters.m_CifgEnabled) |
| { |
| m_InputToInputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo()); |
| |
| m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo()); |
| |
| m_CellToInputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| if (m_Data.m_CellToInputWeights != nullptr) |
| { |
| BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo()); |
| } |
| |
| m_InputGateBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo()); |
| |
| lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(), |
| m_RecurrentToInputWeightsTensor.get(), |
| m_Data.m_CellToInputWeights != nullptr ? m_CellToInputWeightsTensor.get() : nullptr, |
| m_InputGateBiasTensor.get()); |
| } |
| |
| if (m_Data.m_Parameters.m_ProjectionEnabled) |
| { |
| m_ProjectionWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo()); |
| |
| m_ProjectionBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| if (m_Data.m_ProjectionBias != nullptr) |
| { |
| BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo()); |
| } |
| |
| lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(), |
| m_Data.m_ProjectionBias != nullptr ? m_ProjectionBiasTensor.get() : nullptr); |
| } |
| |
| if (m_Data.m_Parameters.m_PeepholeEnabled) |
| { |
| m_CellToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo()); |
| |
| m_CellToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo()); |
| |
| lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get()); |
| } |
| |
| if (m_Data.m_Parameters.m_LayerNormEnabled) |
| { |
| m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| if (!m_Data.m_Parameters.m_CifgEnabled) |
| { |
| BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo()); |
| } |
| |
| m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo()); |
| |
| m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo()); |
| |
| m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo()); |
| |
| lstm_param.set_layer_normalization_params(m_Data.m_Parameters.m_CifgEnabled ? |
| nullptr : m_InputLayerNormWeightsTensor.get(), |
| m_ForgetLayerNormWeightsTensor.get(), |
| m_CellLayerNormWeightsTensor.get(), |
| m_OutputLayerNormWeightsTensor.get()); |
| } |
| |
| const arm_compute::ITensor& input = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| const arm_compute::ITensor& output_state_in = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor(); |
| const arm_compute::ITensor& cell_state_in = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[2])->GetTensor(); |
| |
| arm_compute::ITensor& output_state_out = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[1])->GetTensor(); |
| arm_compute::ITensor& cell_state_out = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->GetTensor(); |
| arm_compute::ITensor& output = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[3])->GetTensor(); |
| |
| // Get the batch_size and the num_units from the cellStateIn dimensions |
| const TensorInfo& inputTensorInfo = info.m_InputTensorInfos[2]; |
| const unsigned int batch_size = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[0]); |
| const unsigned int num_units = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[1]); |
| |
| m_ScratchBuffer = std::make_unique<arm_compute::Tensor>(); |
| if (m_Data.m_Parameters.m_CifgEnabled) |
| { |
| // 2D tensor with dimensions [num_units * 3, batch_size] with CIFG |
| armnn::TensorInfo scratchBuffer1({ batch_size, num_units * 3 }, DataType::Float32); |
| BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer1); |
| } |
| else |
| { |
| // scratch_buffer [num_units * 4, batch_size] without CIFG |
| armnn::TensorInfo scratchBuffer2({ batch_size, num_units * 4 }, DataType::Float32); |
| BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer2); |
| } |
| |
| float cell_threshold = m_Data.m_Parameters.m_ClippingThresCell; |
| float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj; |
| |
| // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations |
| arm_compute::ActivationLayerInfo activationLayerInfo = |
| ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc); |
| |
| m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(), |
| m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(), |
| m_RecurrentToCellWeightsTensor.get(), m_RecurrentToOutputWeightsTensor.get(), |
| m_ForgetGateBiasTensor.get(), m_CellBiasTensor.get(), m_OutputGateBiasTensor.get(), |
| &output_state_in, &cell_state_in, m_ScratchBuffer.get(), &output_state_out, |
| &cell_state_out, &output, lstm_param, activationLayerInfo, |
| cell_threshold, projection_threshold); |
| |
| armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer); |
| |
| InitializeArmComputeTensorData(*m_InputToForgetWeightsTensor, |
| m_Data.m_InputToForgetWeights); |
| InitializeArmComputeTensorData(*m_InputToCellWeightsTensor, |
| m_Data.m_InputToCellWeights); |
| InitializeArmComputeTensorData(*m_InputToOutputWeightsTensor, |
| m_Data.m_InputToOutputWeights); |
| InitializeArmComputeTensorData(*m_RecurrentToForgetWeightsTensor, |
| m_Data.m_RecurrentToForgetWeights); |
| InitializeArmComputeTensorData(*m_RecurrentToCellWeightsTensor, |
| m_Data.m_RecurrentToCellWeights); |
| InitializeArmComputeTensorData(*m_RecurrentToOutputWeightsTensor, |
| m_Data.m_RecurrentToOutputWeights); |
| InitializeArmComputeTensorData(*m_ForgetGateBiasTensor, |
| m_Data.m_ForgetGateBias); |
| InitializeArmComputeTensorData(*m_CellBiasTensor, |
| m_Data.m_CellBias); |
| InitializeArmComputeTensorData(*m_OutputGateBiasTensor, |
| m_Data.m_OutputGateBias); |
| |
| if (!m_Data.m_Parameters.m_CifgEnabled) |
| { |
| InitializeArmComputeTensorData(*m_InputToInputWeightsTensor, |
| m_Data.m_InputToInputWeights); |
| InitializeArmComputeTensorData(*m_RecurrentToInputWeightsTensor, |
| m_Data.m_RecurrentToInputWeights); |
| if (m_Data.m_CellToInputWeights != nullptr) |
| { |
| InitializeArmComputeTensorData(*m_CellToInputWeightsTensor, |
| m_Data.m_CellToInputWeights); |
| } |
| InitializeArmComputeTensorData(*m_InputGateBiasTensor, |
| m_Data.m_InputGateBias); |
| } |
| |
| if (m_Data.m_Parameters.m_ProjectionEnabled) |
| { |
| InitializeArmComputeTensorData(*m_ProjectionWeightsTensor, |
| m_Data.m_ProjectionWeights); |
| if (m_Data.m_ProjectionBias != nullptr) |
| { |
| InitializeArmComputeTensorData(*m_ProjectionBiasTensor, |
| m_Data.m_ProjectionBias); |
| } |
| } |
| |
| if (m_Data.m_Parameters.m_PeepholeEnabled) |
| { |
| InitializeArmComputeTensorData(*m_CellToForgetWeightsTensor, |
| m_Data.m_CellToForgetWeights); |
| InitializeArmComputeTensorData(*m_CellToOutputWeightsTensor, |
| m_Data.m_CellToOutputWeights); |
| } |
| |
| if (m_Data.m_Parameters.m_LayerNormEnabled) |
| { |
| if (!m_Data.m_Parameters.m_CifgEnabled) |
| { |
| InitializeArmComputeTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights); |
| } |
| InitializeArmComputeTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights); |
| InitializeArmComputeTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights); |
| InitializeArmComputeTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights); |
| } |
| |
| // Force Compute Library to perform the necessary copying and reshaping, after which |
| // delete all the input tensors that will no longer be needed |
| m_LstmLayer.prepare(); |
| FreeUnusedTensors(); |
| } |
| |
| void NeonLstmFloatWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonLstmFloatWorkload_Execute", GetGuid()); |
| m_LstmLayer.run(); |
| } |
| |
| arm_compute::Status NeonLstmFloatWorkloadValidate(const TensorInfo& input, |
| const TensorInfo& outputStateIn, |
| const TensorInfo& cellStateIn, |
| const TensorInfo& scratchBuffer, |
| const TensorInfo& outputStateOut, |
| const TensorInfo& cellStateOut, |
| const TensorInfo& output, |
| const LstmDescriptor& descriptor, |
| const LstmInputParamsInfo& paramsInfo) |
| { |
| arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info; |
| |
| // The inputs and outputs |
| const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input); |
| const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn); |
| const arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn); |
| const arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer); |
| const arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut); |
| const arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut); |
| const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output); |
| |
| // Basic parameters |
| const arm_compute::TensorInfo aclInputToForgetWeightsInfo |
| = BuildArmComputeTensorInfo(paramsInfo.GetInputToForgetWeights()); |
| const arm_compute::TensorInfo aclInputToCellWeightsInfo |
| = BuildArmComputeTensorInfo(paramsInfo.GetInputToCellWeights()); |
| const arm_compute::TensorInfo aclInputToOutputWeightsInfo |
| = BuildArmComputeTensorInfo(paramsInfo.GetInputToOutputWeights()); |
| const arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo |
| = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToForgetWeights()); |
| const arm_compute::TensorInfo aclRecurrentToCellWeightsInfo |
| = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToCellWeights()); |
| const arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo |
| = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToOutputWeights()); |
| const arm_compute::TensorInfo aclForgetGateBiasInfo |
| = BuildArmComputeTensorInfo(paramsInfo.GetForgetGateBias()); |
| const arm_compute::TensorInfo aclCellBiasInfo |
| = BuildArmComputeTensorInfo(paramsInfo.GetCellBias()); |
| const arm_compute::TensorInfo aclOutputGateBiasInfo |
| = BuildArmComputeTensorInfo(paramsInfo.GetOutputGateBias()); |
| |
| arm_compute::TensorInfo aclInputToInputWeightsInfo; |
| arm_compute::TensorInfo aclRecurrentToInputWeightsInfo; |
| arm_compute::TensorInfo aclCellToInputWeightsInfo; |
| arm_compute::TensorInfo aclInputGateBiasInfo; |
| arm_compute::TensorInfo aclProjectionWeightsInfo; |
| arm_compute::TensorInfo aclProjectionBiasInfo; |
| arm_compute::TensorInfo aclCellToForgetWeightsInfo; |
| arm_compute::TensorInfo aclCellToOutputWeightsInfo; |
| |
| arm_compute::TensorInfo aclInputLayerNormWeightsInfo; |
| arm_compute::TensorInfo aclForgetLayerNormWeightsInfo; |
| arm_compute::TensorInfo aclCellLayerNormWeightsInfo; |
| arm_compute::TensorInfo aclOutputLayerNormWeightsInfo; |
| |
| |
| if (!descriptor.m_CifgEnabled) |
| { |
| if (descriptor.m_PeepholeEnabled) |
| { |
| aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToInputWeights()); |
| } |
| aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToInputWeights()); |
| aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToInputWeights()); |
| aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputGateBias()); |
| |
| lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo, &aclRecurrentToInputWeightsInfo, |
| descriptor.m_PeepholeEnabled ? &aclCellToInputWeightsInfo : nullptr, |
| &aclInputGateBiasInfo); |
| } |
| |
| if (descriptor.m_ProjectionEnabled) |
| { |
| if (paramsInfo.m_ProjectionBias != nullptr) |
| { |
| aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionBias()); |
| } |
| aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionWeights()); |
| |
| lstm_params_info.set_projection_params(&aclProjectionWeightsInfo, |
| paramsInfo.m_ProjectionBias != nullptr ? |
| &aclProjectionBiasInfo : nullptr); |
| } |
| |
| if (descriptor.m_PeepholeEnabled) |
| { |
| aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToForgetWeights()); |
| aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToOutputWeights()); |
| |
| lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo); |
| } |
| |
| if (descriptor.m_LayerNormEnabled) |
| { |
| if (!descriptor.m_CifgEnabled) |
| { |
| aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputLayerNormWeights()); |
| } |
| aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetForgetLayerNormWeights()); |
| aclCellLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellLayerNormWeights()); |
| aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputLayerNormWeights()); |
| |
| lstm_params_info.set_layer_normalization_params(descriptor.m_CifgEnabled ? |
| nullptr : &aclInputLayerNormWeightsInfo, |
| &aclForgetLayerNormWeightsInfo, |
| &aclCellLayerNormWeightsInfo, |
| &aclOutputLayerNormWeightsInfo); |
| } |
| |
| float cell_threshold = descriptor.m_ClippingThresCell; |
| float projection_threshold = descriptor.m_ClippingThresProj; |
| |
| // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations |
| arm_compute::ActivationLayerInfo activationLayerInfo = |
| ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc); |
| |
| return arm_compute::NELSTMLayer::validate(&aclInputInfo, |
| &aclInputToForgetWeightsInfo, |
| &aclInputToCellWeightsInfo, |
| &aclInputToOutputWeightsInfo, |
| &aclRecurrentToForgetWeightsInfo, |
| &aclRecurrentToCellWeightsInfo, |
| &aclRecurrentToOutputWeightsInfo, |
| &aclForgetGateBiasInfo, |
| &aclCellBiasInfo, |
| &aclOutputGateBiasInfo, |
| &aclOutputStateInInfo, |
| &aclCellStateInInfo, |
| &aclScratchBufferInfo, |
| &aclOutputStateOutInfo, |
| &aclCellStateOutInfo, |
| &aclOutputInfo, |
| lstm_params_info, |
| activationLayerInfo, |
| cell_threshold, |
| projection_threshold); |
| } |
| |
| void NeonLstmFloatWorkload::FreeUnusedTensors() |
| { |
| FreeTensorIfUnused(m_InputToInputWeightsTensor); |
| FreeTensorIfUnused(m_InputToForgetWeightsTensor); |
| FreeTensorIfUnused(m_InputToCellWeightsTensor); |
| FreeTensorIfUnused(m_InputToOutputWeightsTensor); |
| FreeTensorIfUnused(m_RecurrentToInputWeightsTensor); |
| FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor); |
| FreeTensorIfUnused(m_RecurrentToCellWeightsTensor); |
| FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor); |
| FreeTensorIfUnused(m_CellToInputWeightsTensor); |
| FreeTensorIfUnused(m_CellToForgetWeightsTensor); |
| FreeTensorIfUnused(m_CellToOutputWeightsTensor); |
| FreeTensorIfUnused(m_InputGateBiasTensor); |
| FreeTensorIfUnused(m_ForgetGateBiasTensor); |
| FreeTensorIfUnused(m_CellBiasTensor); |
| FreeTensorIfUnused(m_OutputGateBiasTensor); |
| FreeTensorIfUnused(m_ProjectionWeightsTensor); |
| FreeTensorIfUnused(m_ProjectionBiasTensor); |
| FreeTensorIfUnused(m_ScratchBuffer); |
| FreeTensorIfUnused(m_InputLayerNormWeightsTensor); |
| FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor); |
| FreeTensorIfUnused(m_CellLayerNormWeightsTensor); |
| FreeTensorIfUnused(m_OutputLayerNormWeightsTensor); |
| } |
| |
| void NeonLstmFloatWorkload::ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) |
| { |
| ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; |
| this->m_Data.m_Inputs[slot] = tensorHandle; |
| try |
| { |
| Reconfigure(); |
| } |
| catch(armnn::UnimplementedException& e) |
| { |
| // Cannot reconfigure, revert the slot back and throw the exception. |
| this->m_Data.m_Inputs[slot] = backupHandle; |
| throw e; |
| } |
| } |
| |
| // Replace output tensor handle with the given TensorHandle |
| void NeonLstmFloatWorkload::ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) |
| { |
| ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; |
| this->m_Data.m_Inputs[slot] = tensorHandle; |
| try |
| { |
| Reconfigure(); |
| } |
| catch(armnn::UnimplementedException& e) |
| { |
| // Cannot reconfigure, revert the slot back and throw the exception. |
| this->m_Data.m_Inputs[slot] = backupHandle; |
| throw e; |
| } |
| } |
| |
| void NeonLstmFloatWorkload::Reconfigure() |
| { |
| throw armnn::UnimplementedException("Reconfigure not implemented for this workload"); |
| } |
| |
| } //namespace armnn |
